A cross-border e-commerce risk control management method

Abstract

The present invention belongs to the technical field of e-commerce risk control management, and discloses a cross-border e-commerce risk control management method. The present invention comprehensively analyzes the historical price fluctuation index and real-time price deviation index of target commodities corresponding to each merchant in the cross-border e-commerce process, and then obtains the price risk of each merchant. This analysis method can help consumers understand the real price status of the target commodity more clearly, and help consumers make more rational purchasing decisions. The present invention analyzes the comprehensive risk of commodities of each merchant by analyzing the logistics risk, quality risk, after-sales risk and price risk of each merchant. This analysis method comprehensively evaluates the risk of commodities from multiple dimensions, which helps consumers and merchants to understand the risk status of commodities more comprehensively, so as to make more rational decisions, help consumers save time and energy, and improve shopping efficiency. At the same time, sorting by type can provide consumers with multiple risk sorting methods.

Description

Technical Field

[0001] The present invention belongs to the technical field of e-commerce risk control management and relates to a cross-border e-commerce risk control management method. Background Art

[0002] Cross-border e-commerce refers to an international business activity that completes transactions through e-commerce platforms, electronic payment settlements, and the delivery of goods through cross-border e-commerce logistics and remote warehousing. In actual operation, cross-border e-commerce will generate many risks due to factors such as involving multiple countries or long response time. Among them, various types of risks are aimed at merchants, users and other personnel, and consumer-based risk management analysis is the basis and focus of cross-border e-commerce risk management. Therefore, risk management analysis for cross-border e-commerce is of great significance.

[0003] Consumer-based risk management is usually reflected in the process of users screening out merchants who provide security for users in cross-border e-commerce. In this process, the selection of merchants is involved. However, the traditional cross-border e-commerce transaction risk analysis of cross-border e-commerce transaction activities focuses only on the merchant's commodity price risk, without conducting a comprehensive analysis of the various risks of the commodities. This will lead to one-sided analysis results and inaccurate assessment of the overall risk of the commodities. It is difficult for consumers to fully grasp the true risk situation of each merchant and make wrong choices, resulting in economic losses. At the same time, the lack of analysis of multiple types of risks may not meet the actual needs of consumers and reduce the efficiency of consumers in purchasing commodities.

[0004] Traditional cross-border e-commerce transaction risk analysis of merchants' commodity prices usually only involves the vertical price fluctuations of commodities, specifically the analysis of the historical price fluctuations of target commodities. This price analysis method does not take into account the horizontal analysis of the target commodity prices, that is, the position of the target commodity prices in the current market environment, resulting in ignoring the commodity quality reflected by the current price position, which may cause damage to the interests of consumers. Summary of the invention

[0005] In view of this, in order to solve the problems raised in the above background technology, a cross-border e-commerce risk control management method is now proposed.

[0006] The purpose of the present invention can be achieved through the following technical solutions: A cross-border e-commerce risk control management method, comprising: S1, extracting a list of merchants selling the target product based on the target product searched by the user on the cross-border e-commerce platform.

[0007] S2. Extract the historical logistics delivery records of each merchant, wherein the historical logistics delivery records specifically include the estimated delivery time, actual delivery time, estimated arrival time and actual arrival time corresponding to each historical order, and then analyze the logistics risk situation of each merchant.

[0008] S3. Extract the historical product quality information of each merchant, wherein the historical product quality information includes the product return frequency, product return reason and product evaluation score corresponding to each historical order, and then analyze the quality risk situation of each merchant.

[0009] S4. Extract the historical after-sales information of each merchant, wherein the historical after-sales information includes the after-sales evaluation score and after-sales response time corresponding to each historical order, and then analyze the after-sales risk situation of each merchant.

[0010] S5. Analyze the historical price fluctuations of the target commodities of each merchant based on the historical price information of the target commodities corresponding to the merchants, and analyze the real-time price deviations of the target commodities of each merchant based on the real-time prices of the target commodities corresponding to the merchants, and then analyze the price risk of each merchant.

[0011] S6. Analyze the comprehensive risk situation of each merchant’s products based on their logistics risk, quality risk, after-sales risk and price risk.

[0012] S7. Rank the merchants based on their logistics risk, quality risk, after-sales risk, price risk and comprehensive commodity risk. When the user selects a certain ranking method, the corresponding ranking result will be pushed to the user.

[0013] S8. Determine whether each merchant has potential risks based on the comprehensive risk situation of its products. If it is determined that there are potential risks, analyze the risk tendency of the merchant and push it to the user.

[0014] In a preferred embodiment of the present invention, the steps for analyzing the logistics risk situation of each merchant are as follows: the difference between the actual delivery time and the expected delivery time of each historical order in the historical logistics delivery record of each merchant is calculated to obtain the actual delivery time of each historical order, and the actual delivery time of each historical order is compared with the pre-set reference delivery time to obtain the deviation of the actual delivery time of each historical order.

[0015] The actual delivery time deviation of each historical order is averaged to obtain the average delivery time deviation of each merchant.

[0016] The estimated logistics time is obtained by calculating the difference between the estimated arrival time and the estimated delivery time in the historical logistics delivery records of each merchant, and the actual logistics time is obtained by calculating the difference between the actual arrival time and the actual delivery time. The actual logistics time of each historical order is compared with the estimated logistics time to obtain the actual logistics time deviation of each historical order.

[0017] The actual logistics time deviation of each historical order is averaged to obtain the average logistics time deviation of each merchant.

[0018] The average delivery time deviation of each merchant Deviation from the average logistics time Substitute into the formula Get the logistics risk λ of each merchant 物流 , where e represents a natural constant.

[0019] In a preferred embodiment of the present invention, the analysis of the quality risk situation of each merchant is specifically as follows: extracting the product return frequency, product return reasons and product evaluation scores corresponding to each merchant's historical orders, wherein the product return reasons include quality issues and non-quality issues.

[0020] Count the number of historical orders where the reason for product return was quality issues.

[0021] The product evaluation scores corresponding to the historical orders whose reasons for product returns are quality issues are averaged to obtain the average product evaluation score corresponding to the historical orders whose reasons for product returns are quality issues.

[0022] Using the formula Get the quality risk λ of each merchant 质量 , where X represents the number of historical orders in each merchant's historical orders where the reason for the return of goods is quality problems, X0 represents the return frequency corresponding to each merchant's historical orders, D represents the average product evaluation score corresponding to the historical orders where the reason for the return is quality problems, and D0 represents the pre-set product rating threshold.

[0023] In a preferred embodiment of the present invention, the analysis of the after-sales risk situation of each merchant is specifically as follows: extracting the after-sales historical orders corresponding to each merchant that have after-sales.

[0024] The after-sales response time corresponding to each after-sales historical order of each merchant is compared with the preset reference after-sales response time to obtain the deviation degree of the after-sales response time corresponding to the after-sales historical orders of each merchant.

[0025] The average after-sales response time deviation of each merchant's after-sales historical orders is calculated by averaging the after-sales response time deviation of each merchant.

[0026] The average after-sales evaluation score of each merchant’s after-sales historical orders is calculated by averaging the after-sales evaluation scores.

[0027] The average after-sales response time deviation of each merchant Substitute into the formula Get the after-sales risk λ of each merchant 售后 , where S represents the average after-sales evaluation score of each after-sales historical order, and S0 represents the pre-set after-sales rating threshold.

[0028] In a preferred embodiment of the present invention, the steps for analyzing the historical price fluctuations of target products of each merchant are as follows: obtaining the listing time of the target products corresponding to each merchant, and taking the time period from the listing time point of the target products of each merchant to the search time point of the target products as the price reference period.

[0029] With time as the horizontal axis and the target commodity price as the vertical axis, a target commodity price change curve is plotted within each merchant's price reference period.

[0030] The price change curve of the target commodity is evenly distributed to obtain a number of price monitoring points, and the slope of the curve of each price monitoring point is obtained to obtain the price fluctuation index of each price monitoring point.

[0031] The absolute values ​​of the price fluctuation index corresponding to each price monitoring point are averaged to obtain the historical price fluctuation index of the target commodity corresponding to each merchant.

[0032] In a preferred embodiment of the present invention, the step of analyzing the deviation of the real-time price of the target commodity of each merchant is as follows: the real-time price of the target commodity corresponding to each merchant is averaged to obtain the average real-time price of the target commodity.

[0033] The difference between the price of each merchant's corresponding target product and the average real-time price of the target product is calculated to obtain the deviation price of each merchant's target product, and the absolute value of the deviation price of each merchant's target product is compared with the average real-time price of the target product to obtain the real-time price deviation index of each merchant's target product.

[0034] In a preferred embodiment of the present invention, the analysis of the price risk of each merchant is as follows: the historical price fluctuation index of the target commodity of each merchant and real-time price deviation index Substitute into the formula Get the price risk λ of each merchant 价格 , where α1 and α2 represent the weights of the historical price volatility index and the real-time price deviation index respectively.

[0035] In a preferred embodiment of the present invention, the analysis of the comprehensive risk of commodities of each merchant is as follows: the logistics risk of each merchant λ 物流 , quality risk λ 质量 , post-sale risk λ 售后 and price risk λ 价格 Substitute into the formula λ=β1*λ 物流 +β2*λ 质量 +β3*λ 售后 +β4*λ 价格The comprehensive risk λ of the goods of each merchant is obtained, where β1, β2, β3, and β4 represent the weights of logistics risk, quality risk, after-sales risk, and price risk, respectively, and β1+β2+β3+β4=1.

[0036] In a preferred embodiment of the present invention, the analysis of determining whether each merchant has potential risks is as follows: comparing the comprehensive risk of each merchant's goods with the pre-set comprehensive risk of allowed goods. When the comprehensive risk of a merchant's goods is less than or equal to the pre-set comprehensive risk of allowed goods, it is determined that the merchant has no potential risks. When the comprehensive risk of a merchant's goods is greater than the pre-set comprehensive risk of allowed goods, it is determined that the merchant has potential risks.

[0037] In a preferred embodiment of the present invention, the analysis of the risk tendency of the merchant is specifically directed as follows: the merchant determined to have potential risks is recorded as an abnormal merchant.

[0038] The logistics risk, quality risk, after-sales risk and price risk of the abnormal merchant are compared with the pre-set allowable risk respectively. The allowable risk includes the allowable logistics risk, the allowable quality risk, the allowable after-sales risk and the allowable price risk. When a risk of an abnormal merchant is greater than the corresponding allowable risk, the abnormal merchant has this risk orientation.

[0039] The risk levels corresponding to the risk indicators of abnormal merchants are compared, and the risk indicator corresponding to the maximum risk level is selected as the risk tendency indicator of the merchant.

[0040] Compared with the prior art, the beneficial effects of the present invention are as follows: (1) The present invention analyzes the comprehensive risk of each merchant's goods by categorizing the merchant's logistics risk, quality risk, after-sales risk and price risk. This analysis method comprehensively evaluates the merchant's risks from multiple dimensions, which helps consumers and merchants to have a more comprehensive understanding of the risk status of the goods, thereby making more rational decisions, helping consumers save time and energy and improve shopping efficiency. At the same time, by sorting the logistics risk, quality risk, after-sales risk and price risk by type, it can provide consumers with multiple risk sorting methods, thereby enhancing the diversity of risk sorting.

[0041] (2) The present invention obtains the price risk of each merchant by comprehensively analyzing the historical price fluctuation index and real-time price deviation index of the target commodity of each merchant in the cross-border e-commerce process. This analysis method can help consumers understand the real price status of the target commodity more clearly, and help consumers make more rational purchasing decisions. Consumers can compare the price rationality of different merchants and commodities based on the price risk, choose a more cost-effective purchasing plan, and avoid economic losses caused by price fluctuations. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for describing the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0043] Figure 1 The present invention is a flowchart of the steps for implementing the method. DETAILED DESCRIPTION

[0044] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0045] See also Figure 1 As shown, the present invention provides a cross-border e-commerce risk control management method, including: S1, extracting a list of merchants selling the target product based on the target product searched by the user on the cross-border e-commerce platform.

[0046] S2. Extract the historical logistics delivery records of each merchant, wherein the historical logistics delivery records specifically include the estimated delivery time, actual delivery time, estimated arrival time and actual arrival time corresponding to each historical order, and then analyze the logistics risk situation of each merchant.

[0047] Preferably, the difference between the actual delivery time and the expected delivery time of each historical order in the historical logistics delivery record of each merchant is calculated to obtain the actual delivery time of each historical order, and the actual delivery time of each historical order is compared with the pre-set reference delivery time to obtain the actual delivery time deviation of each historical order.

[0048] It should be added that using the formula Get the actual delivery time deviation ΔT of each historical order 发货 , where T 发货 Indicates the actual delivery time of each historical order, T 发货0 Indicates the preset reference delivery time.

[0049] For example, T 发货0 =24h.

[0050] The actual delivery time deviation of each historical order is averaged to obtain the average delivery time deviation of each merchant.

[0051] The estimated logistics time is obtained by calculating the difference between the estimated arrival time and the estimated delivery time in the historical logistics delivery records of each merchant, and the actual logistics time is obtained by calculating the difference between the actual arrival time and the actual delivery time. The actual logistics time of each historical order is compared with the estimated logistics time to obtain the actual logistics time deviation of each historical order.

[0052] It should be added that the use of Get the actual delivery time deviation ΔT of each historical order 物流 , where T 物流 Indicates the actual logistics time of each historical order, T 物流0 Indicates the estimated logistics time.

[0053] For example, T 发货0 =24h.

[0054] The actual logistics time deviation of each historical order is averaged to obtain the average logistics time deviation of each merchant.

[0055] The average delivery time deviation of each merchant Deviation from the average logistics time Substitute into the formula Get the logistics risk λ of each merchant 物流 , where e represents a natural constant.

[0056] It should be noted that the reasons for choosing delivery time deviation and logistics time deviation as influencing factors of logistics risk of each merchant are: 1. Delivery time deviation directly reflects the time fluctuation from the merchant receiving the order to the shipment of the goods, while logistics time deviation reflects the time fluctuation from the shipment of the goods to the delivery to the consumer. These two indicators can directly reveal the efficiency stability of the merchant in the logistics process; 2. There are various uncertainties in the logistics process, such as order processing speed, inventory status, transportation delays, etc., which may lead to fluctuations in delivery and logistics time. Delivery time deviation and logistics time deviation can quantify these uncertainties into specific values, which is convenient for merchants and consumers to evaluate logistics risks.

[0057] S3. Extract the historical product quality information of each merchant, wherein the historical product quality information includes the product return frequency, product return reason and product evaluation score corresponding to each historical order, and then analyze the quality risk situation of each merchant.

[0058] Preferably, the commodity return frequency, commodity return reasons and commodity evaluation scores corresponding to each merchant's historical orders are extracted, and the commodity return reasons include quality issues and non-quality issues.

[0059] Count the number of historical orders where the reason for product return was quality issues.

[0060] Exemplary reasons for product returns include product not matching the picture, product being damaged, product having surface scratches, product no longer wanted, unexplained return, and product quantity not matching. Among them, reasons for returns due to quality issues include product being damaged and product having surface scratches.

[0061] The product evaluation scores corresponding to the historical orders whose reasons for product returns are quality issues are averaged to obtain the average product evaluation score corresponding to the historical orders whose reasons for product returns are quality issues.

[0062] Using the formula Get the quality risk λ of each merchant 质量 , where X represents the number of historical orders in each merchant's historical orders where the reason for the return of goods is quality problems, X0 represents the return frequency corresponding to each merchant's historical orders, D represents the average product evaluation score corresponding to the historical orders where the reason for the return is quality problems, and D0 represents the pre-set product rating threshold.

[0063] It should be noted that the reasons for choosing the number of historical orders with quality issues as the reason for product return and the product evaluation score as the factors affecting the merchant's quality risk are: 1. Quality issues among the reasons for product returns are one of the most direct indicators of product quality. If a large number of consumers return products due to quality issues, it means that there are serious problems with the quality of the product, and the merchant's quality risk will increase accordingly; 2. The product evaluation score is the consumer's comprehensive evaluation of product quality. A low score usually means that the product has quality problems or does not meet consumer expectations, while a high score means the opposite.

[0064] It should be explained that the product rating threshold refers to the full score of the product rating system set by the merchant. When the product rating system set by the merchant is 10 points, the product rating threshold is 10 points.

[0065] S4. Extract the historical after-sales information of each merchant, wherein the historical after-sales information includes the after-sales evaluation score and after-sales response time corresponding to each historical order, and then analyze the after-sales risk situation of each merchant.

[0066] Preferably, the after-sales historical orders corresponding to each merchant that have after-sales service are extracted.

[0067] The after-sales response time corresponding to each after-sales historical order of each merchant is compared with the preset reference after-sales response time to obtain the deviation degree of the after-sales response time corresponding to the after-sales historical orders of each merchant.

[0068] It should be added that through Get the deviation of after-sales response time τ of each merchant corresponding to the after-sales historical orders, where T 售后 Indicates the after-sales response time corresponding to each merchant’s after-sales historical order, T 售后0Indicates the preset reference after-sales response time.

[0069] Exemplarily, T0=3 min.

[0070] The average after-sales response time deviation of each merchant's after-sales historical orders is calculated by averaging the after-sales response time deviation of each merchant.

[0071] The average after-sales evaluation score of each merchant’s after-sales historical orders is calculated by averaging the after-sales evaluation scores.

[0072] The average after-sales response time deviation of each merchant Substitute into the formula Get the after-sales risk λ of each merchant 售后 , where S represents the average after-sales evaluation score of each after-sales historical order, and S0 represents the pre-set after-sales rating threshold.

[0073] It should be noted that the reasons for choosing after-sales evaluation scores and after-sales response time as influencing factors of merchants' after-sales risk are: 1. The after-sales evaluation score is a direct feedback from consumers on the quality of merchants' after-sales service. A high-score evaluation usually means that consumers are satisfied with the merchant's after-sales service, while a low-score evaluation may mean that there are problems or deficiencies in the service; 2. The after-sales response time reflects the merchant's response speed after receiving consumers' after-sales needs. A shorter response time usually means that the merchant can quickly solve consumers' problems and provide timely and effective after-sales services.

[0074] It needs to be explained that the after-sales rating threshold refers to the full score of the after-sales rating system set by the merchant. When the after-sales rating system set by the merchant is 10 points, the after-sales rating threshold is 10 points.

[0075] S5. Analyze the historical price fluctuations of the target commodities of each merchant based on the historical price information of the target commodities corresponding to the merchants, and analyze the real-time price deviations of the target commodities of each merchant based on the real-time prices of the target commodities corresponding to the merchants, and then analyze the price risk of each merchant.

[0076] Preferably, the listing time of the target product corresponding to each merchant is obtained, and the time period from the listing time point of the target product of each merchant to the searching time point of the target product is used as the price reference period.

[0077] With time as the horizontal axis and the target commodity price as the vertical axis, a target commodity price change curve is plotted within each merchant's price reference period.

[0078] The price change curve of the target commodity is evenly distributed to obtain a number of price monitoring points, and the slope of the curve of each price monitoring point is obtained to obtain the price fluctuation index of each price monitoring point.

[0079] The absolute values ​​of the price fluctuation index corresponding to each price monitoring point are averaged to obtain the historical price fluctuation index of the target commodity corresponding to each merchant.

[0080] It should be noted that the slope of the target commodity price change curve reflects the price fluctuation of the target commodity. When the slope is large, it indicates that the price fluctuation of the target commodity at that point is large. When the slope approaches 0, it indicates that the price fluctuation of the target commodity at that point is relatively gentle.

[0081] It should be added that the reason for analyzing the price volatility index based on the slope of the target commodity price change curve is that the price volatility of the target commodity can be displayed more intuitively and accurately through the slope of the curve.

[0082] Further preferably, the real-time prices of the target products of the merchants are averaged to obtain the average real-time price of the target products.

[0083] The difference between the price of each merchant's corresponding target product and the average real-time price of the target product is calculated to obtain the deviation price of each merchant's target product, and the absolute value of the deviation price of each merchant's target product is compared with the average real-time price of the target product to obtain the real-time price deviation index of each merchant's target product.

[0084] It should be added that using the formula Get the real-time price deviation index of each merchant's target product Where K 偏离 represents the deviation price of each merchant’s target product, K 实时 Indicates the average real-time price of the target product.

[0085] Further preferably, the historical price fluctuation index of each merchant's target commodity and real-time price deviation index Substitute into the formula Get the price risk λ of each merchant 价格 , where α1 and α2 represent the weights of the historical price volatility index and the real-time price deviation index respectively.

[0086] Exemplarily, α1=0.4, α2=0.6.

[0087] It should be noted that the historical price volatility index has an impact: first, the historical price volatility index directly affects the merchant's assessment of the commodity price risk. When the price volatility index is high, it means that the merchant's commodity prices have experienced large fluctuations in the past, which increases the possibility of further price fluctuations in the future, thereby increasing the merchant's commodity price risk; secondly, through the analysis of the historical price volatility index, consumers can understand the laws and trends of the merchant's commodity price fluctuations and predict possible future price trends.

[0088] It should be noted that vertical price fluctuation refers to the price difference between each merchant's target products and other merchants' target products at that moment, and horizontal price fluctuation refers to the historical selling price fluctuation of a merchant's target products.

[0089] It should be noted that the impact of the real-time price deviation index is immediate and critical to the commodity price risk of merchants. The real-time price deviation index measures the degree of deviation between the current market price and its historical average price. The real-time price deviation index can instantly reflect the changing trend of market prices and help consumers quickly perceive the existence of price risks.

[0090] It should be noted that the present invention obtains the price risk of each merchant by comprehensively analyzing the historical price fluctuation index and real-time price deviation index of the target commodity corresponding to each merchant in the cross-border e-commerce process. This analysis method can help consumers understand the real price status of the target commodity more clearly, and help consumers make more rational purchasing decisions. Consumers can compare the price rationality of different merchants and commodities based on the price risk, choose a more cost-effective purchasing plan, and avoid economic losses caused by price fluctuations.

[0091] S6. Analyze the comprehensive risk situation of each merchant’s products based on their logistics risk, quality risk, after-sales risk and price risk.

[0092] Preferably, the logistics risk of each merchant is 物流 , quality risk λ 质量 , post-sale risk λ 售后 and price risk λ 价格 Substitute into the formula λ=β1*λ 物流 +β2*λ 质量 +β3*λ 售后 +β4*λ 价格 The comprehensive risk λ of the goods of each merchant is obtained, where β1, β2, β3, and β4 represent the weights of logistics risk, quality risk, after-sales risk, and price risk, respectively, and β1+β2+β3+β4=1.

[0093] It should be noted that the impact of logistics risk, quality risk, after-sales risk and price risk are as follows: 1. Logistics risk refers to various uncertainties that may be encountered in the logistics process, which may cause damage to goods, delayed delivery or increased costs. For consumers, the level of logistics risk directly affects the shopping experience and satisfaction; 2. Quality risk involves quality issues of the goods themselves, including product defects, non-compliance with standards or inconsistent descriptions. For consumers, the level of quality risk determines whether the goods they purchase meet their expectations; 3. After-sales risk refers to problems that may be encountered after the sale of goods, such as inadequate after-sales service, unclear warranty period or poor repair service. For consumers, good after-sales service is one of the important considerations for them to choose to buy goods; 4. Price risk involves changes in the merchant's commodity prices, including price increases, price increases after the end of promotional activities, etc. For consumers, the level of price risk affects their purchasing power and purchasing decisions.

[0094] Exemplarily, β1=0.2, β2=0.2, β3=0.25, β4=0.35.

[0095] It should be added that the present invention analyzes the comprehensive risk of the goods of each merchant by categorizing the logistics risk, quality risk, after-sales risk and price risk of each merchant. This analysis method comprehensively evaluates the risks of merchants from multiple dimensions, which helps consumers and merchants to have a more comprehensive understanding of the risk status of goods, so as to make more rational decisions, help consumers save time and energy, and improve shopping efficiency. At the same time, by sorting the logistics risk, quality risk, after-sales risk and price risk by type, it can provide consumers with multiple risk sorting methods and enhance the diversification of risk sorting.

[0096] S7. Rank the merchants based on their logistics risk, quality risk, after-sales risk, price risk and comprehensive commodity risk. When the user selects a certain ranking method, the corresponding ranking result will be pushed to the user.

[0097] For example, when the user chooses to sort by price risk, the system will push to the user the corresponding product list of the merchant sorted by price risk.

[0098] S8. Determine whether each merchant has potential risks based on the comprehensive risk situation of its products. If it is determined that there are potential risks, analyze the risk tendency of the merchant and push it to the user.

[0099] Preferably, the comprehensive risk of each merchant's goods is compared with the pre-set allowable comprehensive risk of goods. When the comprehensive risk of a merchant's goods is less than or equal to the pre-set allowable comprehensive risk of goods, it is judged that the merchant has no risk hazards. When the comprehensive risk of a merchant's goods is greater than the pre-set allowable comprehensive risk of goods, it is judged that the merchant has risk hazards.

[0100] For example, the comprehensive risk of the commodity is allowed to be 0.25.

[0101] Further preferably, merchants that are judged to have potential risks are recorded as abnormal merchants.

[0102] The logistics risk, quality risk, after-sales risk and price risk of the abnormal merchant are compared with the pre-set allowable risk respectively. The allowable risk includes the allowable logistics risk, the allowable quality risk, the allowable after-sales risk and the allowable price risk. When a risk of an abnormal merchant is greater than the corresponding allowable risk, the abnormal merchant has this risk orientation.

[0103] The risk levels corresponding to the risk indicators of abnormal merchants are compared, and the risk indicator corresponding to the maximum risk level is selected as the risk tendency indicator of the merchant.

[0104] It should be noted that obtaining risk tendency indications can help users quickly identify the merchant's biggest risk issues, thereby helping consumers choose merchants.

[0105] Exemplarily, when the risk corresponding to the maximum risk level of an abnormal merchant is quality risk, the risk tendency of the merchant is quality risk.

[0106] It should be added that the historical logistics delivery records, historical product quality information, historical after-sales information and historical price information of the target products of each merchant mentioned in the present invention are all stored in the cloud database corresponding to the cross-border e-commerce platform.

[0107] The above contents are merely examples and explanations of the concept of the present invention. Those skilled in the art may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined by the present invention, they shall all fall within the protection scope of the present invention.

Claims

1. A cross-border e-commerce risk control management method, characterized in that: include: S1. Extract a list of merchants selling the target product based on the target product searched by the user on the cross-border e-commerce platform; S2. Extract the historical logistics shipment records of each merchant, wherein the historical logistics shipment records specifically include the estimated shipment time, actual shipment time, estimated arrival time, and actual arrival time corresponding to each historical order, and then analyze the logistics risk situation of each merchant; S3. Extracting historical product quality information of each merchant, wherein the historical product quality information includes the product return frequency, product return reason and product evaluation score corresponding to each historical order, and then analyzing the quality risk of each merchant; S4. Extracting the historical after-sales information of each merchant, wherein the historical after-sales information includes the after-sales evaluation score and after-sales response time corresponding to each historical order, and then analyzing the after-sales risk situation of each merchant; S5. Analyze the historical price fluctuations of the target commodities of each merchant based on the historical price information of the corresponding target commodities of the merchants, and analyze the real-time price deviations of the target commodities of each merchant based on the real-time prices of the corresponding target commodities of each merchant, and then analyze the price risk of each merchant; S6. Analyze the comprehensive risk of each merchant's goods based on their logistics risk, quality risk, after-sales risk and price risk; S7, sorting the merchants based on their logistics risk, quality risk, after-sales risk, price risk and comprehensive commodity risk, and when the user selects a certain sorting method, the corresponding sorting result is pushed to the user; S8. Determine whether each merchant has hidden risks based on the comprehensive risk situation of the goods of each merchant. If it is determined that there are hidden risks, analyze the risk tendency of the merchant and push it to the user; The steps for analyzing the logistics risk of each merchant are as follows: The actual delivery time of each historical order in each merchant's historical logistics delivery record is calculated by the difference between the actual delivery time and the expected delivery time to obtain the actual delivery time of each historical order, and the actual delivery time of each historical order is compared with the preset reference delivery time to obtain the deviation degree of the actual delivery time of each historical order; The actual delivery time deviation of each historical order is averaged to obtain the average delivery time deviation of each merchant; The estimated arrival time in each merchant's historical logistics delivery record is calculated by the difference between the estimated delivery time to obtain the estimated logistics time, and the actual arrival time is calculated by the difference between the actual delivery time to obtain the actual logistics time. The actual logistics time of each historical order is compared with the estimated logistics time to obtain the actual logistics time deviation of each historical order. The actual logistics time deviation of each historical order is averaged to obtain the average logistics time deviation of each merchant; The average delivery time deviation of each merchant Deviation from the average logistics time Substitute into the formula Get the logistics risk of each merchant ,in represents a natural constant; The analysis of the quality risk of each merchant is as follows: Extract the product return frequency, product return reasons and product evaluation scores corresponding to each merchant's historical orders, where the product return reasons include quality issues and non-quality issues; Count the number of historical orders where the reason for product return was quality issues; The average product evaluation scores of historical orders whose reasons for product returns are quality issues are calculated by averaging the product evaluation scores corresponding to the historical orders whose reasons for product returns are quality issues; Using the formula Get the quality risk of each merchant ,in Indicates the number of historical orders from each merchant whose reasons for product returns were quality issues. Indicates the return frequency of each merchant’s historical orders. Indicates the average product evaluation score corresponding to historical orders whose return reason was quality problem. Indicates the preset product rating threshold; The analysis of the after-sales risks of each merchant is as follows: Extract the after-sales historical orders corresponding to each merchant; Compare the after-sales response time corresponding to each after-sales historical order of each merchant with the preset reference after-sales response time to obtain the deviation degree of the after-sales response time corresponding to the after-sales historical orders of each merchant; The average deviation of after-sales response time of each merchant's after-sales historical order is calculated to obtain the average deviation of after-sales response time of each merchant; The average after-sales evaluation score of each merchant's after-sales historical orders is calculated to obtain the average after-sales evaluation score of each merchant; The average after-sales response time deviation of each merchant Substitute into the formula Get the after-sales risk of each merchant ,in Represents the average after-sales evaluation score of each after-sales historical order, Indicates the preset after-sales rating threshold; The steps for analyzing the historical price fluctuations of target commodities of each merchant are as follows: Obtain the listing time of the target products of each merchant, and use the time period from the listing time of the target products of each merchant to the search time of the target products as the price reference period; Using time as the horizontal axis and the target commodity price as the vertical axis, draw the target commodity price change curve within the price reference period of each merchant; Evenly distribute the price change curve of the target commodity to obtain a number of price monitoring points, and obtain the slope of the curve of each price monitoring point to obtain the price fluctuation index of each price monitoring point; The absolute value of the price fluctuation index corresponding to each price monitoring point is averaged to obtain the historical price fluctuation index of the target commodity corresponding to each merchant; The steps for analyzing the real-time price deviation of each merchant's target product are as follows: The real-time price of the target product corresponding to each merchant is averaged to obtain the average real-time price of the target product; The difference between the price of the target product of each merchant and the average real-time price of the target product is calculated to obtain the deviation price of the target product of each merchant, and the absolute value of the deviation price of the target product of each merchant is compared with the average real-time price of the target product to obtain the real-time price deviation index of the target product of each merchant; The analysis of the price risk of each merchant is as follows: The historical price fluctuation index of each merchant's target product and real-time price deviation index Substitute into the formula Get the price risk of each merchant ,in Respectively represent the weights of the historical price volatility index and the real-time price deviation index; The analysis of the comprehensive risk of each merchant's goods is as follows: The logistics risk of each merchant , Quality Risk , After-sales risk and price risk Substitute into the formula Get the comprehensive risk of each merchant's goods ,in They represent the weights of logistics risk, quality risk, after-sales risk and price risk respectively. .

2. A cross-border e-commerce risk management method as claimed in claim 1, characterized in that: The analysis of whether each merchant has risk hazards is as follows: The comprehensive risk of each merchant's goods is compared with the pre-set comprehensive risk of the allowed goods. When the comprehensive risk of a merchant's goods is less than or equal to the pre-set comprehensive risk of the allowed goods, it is judged that the merchant has no risk hazards. When the comprehensive risk of a merchant's goods is greater than the pre-set comprehensive risk of the allowed goods, it is judged that the merchant has risk hazards.

3. A cross-border e-commerce risk management method as claimed in claim 1, characterized in that: The analysis of the merchant's risk tendency points to the following: Merchants that are judged to have potential risks will be recorded as abnormal merchants; Compare the logistics risk, quality risk, after-sales risk and price risk of the abnormal merchant with the preset allowable risk respectively, wherein the allowable risk includes the allowable logistics risk, the allowable quality risk, the allowable after-sales risk and the allowable price risk. When a risk of the abnormal merchant is greater than the corresponding allowable risk, the abnormal merchant has the risk direction; The risk levels corresponding to the risk indicators of abnormal merchants are compared, and the risk indicator corresponding to the maximum risk level is selected as the risk tendency indicator of the merchant.

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